[[["์ดํดํ๊ธฐ ์ฌ์","easyToUnderstand","thumb-up"],["๋ฌธ์ ๊ฐ ํด๊ฒฐ๋จ","solvedMyProblem","thumb-up"],["๊ธฐํ","otherUp","thumb-up"]],[["์ดํดํ๊ธฐ ์ด๋ ค์","hardToUnderstand","thumb-down"],["์๋ชป๋ ์ ๋ณด ๋๋ ์ํ ์ฝ๋","incorrectInformationOrSampleCode","thumb-down"],["ํ์ํ ์ ๋ณด/์ํ์ด ์์","missingTheInformationSamplesINeed","thumb-down"],["๋ฒ์ญ ๋ฌธ์ ","translationIssue","thumb-down"],["๊ธฐํ","otherDown","thumb-down"]],["์ต์ข ์ ๋ฐ์ดํธ: 2025-09-03(UTC)"],[[["\u003cp\u003eDe-identification involves removing identifying information from data, such as protected health information (PHI), in DICOM instances and FHIR resources using the Cloud Healthcare API to mask, delete, or obscure the data.\u003c/p\u003e\n"],["\u003cp\u003eDe-identification can be performed at the dataset level, FHIR store level, or DICOM store level, and the process does not affect the original data, creating copies in destination locations instead.\u003c/p\u003e\n"],["\u003cp\u003eThe Cloud Healthcare API can process data in a location that differs from where the source and destination reside, but the final data will be stored in the same Google Cloud location as the source.\u003c/p\u003e\n"],["\u003cp\u003eDICOM de-identification can remove specific tags containing sensitive data and use OCR to redact text in images, whereas FHIR de-identification can remove specific values or process text to selectively remove only sensitive portions.\u003c/p\u003e\n"],["\u003cp\u003eThe de-identification process is useful when sharing health information with non-privileged parties, creating datasets for analysis from multiple sources, or anonymizing data for machine learning.\u003c/p\u003e\n"]]],[],null,["# Data de-identification\n\nDe-identification is the process of removing identifying information from data.\nThe Cloud Healthcare API detects sensitive data in\n[DICOM instances](/healthcare-api/docs/how-tos/dicom-deidentify)\nand [FHIR resources](/healthcare-api/docs/how-tos/fhir-deidentify), such as protected\nhealth information (PHI), and then uses a de-identification transformation to\nmask, delete, or otherwise obscure the data. De-identification has multiple\nuses cases, including:\n\n- When sharing health information with non-privileged parties\n- When creating datasets from multiple sources and analyzing them\n- When anonymizing data so that it can be used in machine learning models\n\nDe-identification overview\n--------------------------\n\n\nDe-identification works at the following levels:\n\n- At the dataset level. De-identification occurs on all data in DICOM stores and FHIR stores in the dataset. If a dataset contains both DICOM instances and FHIR resources, you can de-identify all of the instances and resources at the same time. \n\n To de-identify sensitive data at the dataset level, call the Cloud Healthcare API [`datasets.deidentify`](/healthcare-api/docs/reference/rest/v1/projects.locations.datasets/deidentify) method.\n- At the FHIR store level. De-identification occurs on all data in a specific FHIR store in a dataset. \n\n To de-identify sensitive data at the FHIR store level, call the Cloud Healthcare API [`fhirStores.deidentify`](/healthcare-api/docs/reference/rest/v1/projects.locations.datasets.fhirStores/deidentify) method.\n- At the DICOM store level. De-identification occurs on all data in a specific DICOM store in a dataset. \n\n To de-identify sensitive data at the DICOM store level, call the Cloud Healthcare API [`dicomStores.deidentify`](/healthcare-api/docs/reference/rest/v1/projects.locations.datasets.dicomStores/deidentify) method.\n\n\nDe-identification doesn't impact the original dataset, FHIR store, DICOM store,\nor the original data. Depending on how you configure the de-identification, the\noperation behaves as follows:\n\n- If you are de-identifying data at the dataset level, de-identified copies of the original data are written to a new dataset called the *destination dataset*.\n- If you are de-identifying data at the DICOM or FHIR store level, de-identified copies of the original data are written to an existing DICOM or FHIR store in an existing dataset. The output DICOM store and FHIR store are called the *destination DICOM store* and *destination FHIR store*, respectively.\n\n\nThe source dataset, FHIR store, or DICOM store and the destination\ndataset, FHIR store, or DICOM store must reside in\nthe same Google Cloud location. De-identifying data across\nmultiple Google Cloud locations is not supported.\n\nDe-identification location\n--------------------------\n\n\nWhen the Cloud Healthcare API de-identifies data, the data might be processed in a location that is different from where the source and destination FHIR or DICOM store resides.\nAfter de-identification finishes, the data is stored in the same Google Cloud location as the source FHIR store or DICOM store.\n\n\nTo ensure data is processed in the same location as the source FHIR or DICOM store, you can specify\nthe `useRegionalDataProcessing` option in\n[`DeidentifyConfig`](/healthcare-api/docs/reference/rest/v1/projects.locations.datasets.fhirStores#DeidentifyConfig).\n\nDe-identifying data in the Google Cloud console\n-----------------------------------------------\n\n\nYou can de-identify data for a dataset, FHIR store, or DICOM store from within the Google Cloud console. For more information see [De-identifying data in the Google Cloud console (DICOM)](/healthcare-api/docs/how-tos/dicom-deidentify#de-identifying_data_in_the)\nand [De-identifying data in the Google Cloud console (FHIR)](/healthcare-api/docs/how-tos/fhir-deidentify#de-identifying_data_in_the).\n\nDICOM de-identification\n-----------------------\n\nA DICOM instance contains a set of key-value metadata elements (known as\n*tags* ), and one or more images. The `deidentify` operation can remove specific\ntags that contain sensitive data. The operation can also use automated optical\ncharacter recognition (OCR) to redact burnt-in text on images contained in\nDICOM instances.\n\nFor examples of how to de-identify DICOM data, see [De-identifying DICOM data](/healthcare-api/docs/how-tos/dicom-deidentify).\n\nFHIR de-identification\n----------------------\n\nEach FHIR resource is a JSON-like object that contains key-value elements.\nSome elements are standardized, while others are free text. You can use the\n`deidentify` operation to achieve one of the following results:\n\n- Remove specific values in the resource\n\n- Process the arbitrary text portions to remove only the sensitive portions,\n leaving the rest of the data as is\n\nFor examples of how to de-identify FHIR data, see [De-identifying FHIR data](/healthcare-api/docs/how-tos/fhir-deidentify)."]]
